Advanced Robot Communications through Video: Video Compression Approaches?
Best insight from top research papers
Advanced robot communications through video benefit from innovative video compression approaches. Various studies propose methods to reduce data size for efficient transmission. These approaches include utilizing H.264 compression for range data streams from sensors like Kinect cameras and Velodyne 3D laser scanners, joint compression schemes for multiple robot videos with flexible rate control and priority adjustment, predictive compression algorithms based on drone trajectory for real-time processing and bandwidth reduction, and incorporating robot odometry into video pipelines to selectively reduce video quality without affecting task performance. Additionally, a novel video communication method transmits only essential components for robot recognition, reducing data size for inter-robot communications. These diverse strategies aim to enhance robot communication efficiency and effectiveness through advanced video compression techniques.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
11 Jun 2018 9 Citations | Model Predictive Compression for Drone Video Analytics proposes a real-time compression algorithm based on predicted drone trajectory to optimize image transmission, reducing network bandwidth by 50-72% while maintaining high-quality mosaics. |
| Context-aware video compression incorporating robot odometry enhances robot responsiveness by selectively reducing video quality without affecting task performance, improving control and bandwidth efficiency in robot communications. | |
01 Oct 2008 1 Citations | The paper proposes a video compression method for robot communication, transmitting only essential components for robot recognition, reducing data size for robot-to-robot communication while enabling human-to-human video signal transmission. |
06 Nov 2014 21 Citations | H.264 compression effectively reduces range data stream sizes for remote robot operations, enabling advanced communication through video with high-quality results and practical implementation. |
| Joint multi-video compression scheme in the research optimizes encoding quality and bandwidth allocation for multiple robot videos, enhancing advanced robot communications through efficient video compression approaches. |
Related Questions
Can each level of tilled pyramid tiff images have different compression levels?5 answersYes, each level of tiled pyramid TIFF images can have different compression levels. The pyramidal image compression method allows for the compression of different levels of the pyramid representation of an image. The parameterized NEDI algorithm, proposed by Gashnikov and Gashnikov, is used to interpolate heavily thinned levels of the pyramidal representation, improving the efficiency of the compression method. Additionally, the machine learning-based image compression method developed by Gashnikov and Sasson utilizes the structural features of each layer of the pyramid for machine learning, allowing for sequential compression of the layers. The method also incorporates super-resolution neural network algorithms to improve the efficiency of interpolation for all image pyramid layers. Therefore, different compression levels can be achieved for each level of the tiled pyramid TIFF images using these methods.
How are generative AI techniques being utilized to enhance the performance and capabilities of robots?4 answersGenerative AI techniques are being utilized to enhance the performance and capabilities of robots in various ways. In the context of simulating robot performance and image-based perception, generative adversarial networks (GANs) have been used to reduce the sim-to-real gap and improve image perception. By using GANs, simulated images can be enhanced to closely resemble real-world images, thus improving the accuracy and reliability of robot perception systems. Additionally, generative language models such as GPT-3 have been employed to enhance interactive learning with social robots. These models enable social robots to generate explanations, questions, corrections, and answers, facilitating fluent communication and open-ended question answering. The use of generative AI techniques in robotics holds great potential for improving robot performance, enabling them to better adapt to real-world environments and engage in more natural and effective interactions with humans.
How can auto encoders be used for data compression?5 answersAuto-encoders can be used for data compression by leveraging their ability to learn efficient representations of input data. These models consist of an encoder and a decoder, which work together to compress and reconstruct the data. The encoder compresses the input data into a lower-dimensional representation, while the decoder reconstructs the original data from this compressed representation. This process allows for the reduction of data size while preserving important information. Several papers discuss the use of auto-encoders for data compression in different domains. Li et al. propose cascade decoders-based auto-encoders for image reconstruction, achieving gradually lossless image recovery. Kumble and Patil present an Improved Stacked RBM Auto-Encoder model for compressing sensor data in wireless sensor networks, achieving high compression efficiency and reconstruction accuracy. Lee et al. propose a frequency-selective AE model for compressing power data in the IoT environment, improving reconstruction performance based on spectral properties.
How can autoencoders be used to compress images?3 answersAutoencoders can be used to compress images by using neural network models. One approach is to use a two-layered autoencoder model for video coding, where the multi-layer encoder provides scalability and allows for decoupling the traditional video coding implementation from the neural network solutions. This approach enables the base layer bitstream to be decoded without running the decoding process with the neural network, providing better compression rates and quality results. Another approach is to use a three-layer autoencoder model for compressing and denoising grayscale medical images. This model adds Gaussian noise to the images, passes them through convolution and max pool layers, and then reverses the process for image denoising. The trained model achieves high peak signal-to-noise ratio (PSNR) and compression ratios, making it effective for medical image analysis.
How do I save a robots txt file?7 answers
Who is the director of film Robot?5 answers